Overview

Dataset statistics

Number of variables14
Number of observations323881
Missing cells49706
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.6 MiB
Average record size in memory112.0 B

Variable types

Categorical3
DateTime1
Numeric9
Text1

Alerts

VERSIE has constant value ""Constant
DATUM_BESTAND has constant value ""Constant
PEILDATUM has constant value ""Constant
BEHANDELEND_SPECIALISME_CD is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
AANTAL_PAT_PER_ZPD is highly overall correlated with AANTAL_SUBTRAJECT_PER_ZPDHigh correlation
AANTAL_SUBTRAJECT_PER_ZPD is highly overall correlated with AANTAL_PAT_PER_ZPDHigh correlation
AANTAL_PAT_PER_DIAG is highly overall correlated with AANTAL_SUBTRAJECT_PER_DIAGHigh correlation
AANTAL_SUBTRAJECT_PER_DIAG is highly overall correlated with AANTAL_PAT_PER_DIAGHigh correlation
AANTAL_PAT_PER_SPC is highly overall correlated with BEHANDELEND_SPECIALISME_CD and 1 other fieldsHigh correlation
AANTAL_SUBTRAJECT_PER_SPC is highly overall correlated with AANTAL_PAT_PER_SPCHigh correlation
GEMIDDELDE_VERKOOPPRIJS has 49706 (15.3%) missing valuesMissing
AANTAL_SUBTRAJECT_PER_ZPD is highly skewed (γ1 = 21.00580727)Skewed

Reproduction

Analysis started2023-05-24 08:19:25.412602
Analysis finished2023-05-24 08:19:48.074046
Duration22.66 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

VERSIE
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1.0
323881 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters971643
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 323881
100.0%

Length

2023-05-24T08:19:48.149022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-24T08:19:48.303293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 323881
100.0%

Most occurring characters

ValueCountFrequency (%)
1 323881
33.3%
. 323881
33.3%
0 323881
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 647762
66.7%
Other Punctuation 323881
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 323881
50.0%
0 323881
50.0%
Other Punctuation
ValueCountFrequency (%)
. 323881
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 971643
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 323881
33.3%
. 323881
33.3%
0 323881
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 971643
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 323881
33.3%
. 323881
33.3%
0 323881
33.3%

DATUM_BESTAND
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-05-10
323881 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3238810
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-10
2nd row2023-05-10
3rd row2023-05-10
4th row2023-05-10
5th row2023-05-10

Common Values

ValueCountFrequency (%)
2023-05-10 323881
100.0%

Length

2023-05-24T08:19:48.427851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-24T08:19:48.580876image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-10 323881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 971643
30.0%
2 647762
20.0%
- 647762
20.0%
3 323881
 
10.0%
5 323881
 
10.0%
1 323881
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2591048
80.0%
Dash Punctuation 647762
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 971643
37.5%
2 647762
25.0%
3 323881
 
12.5%
5 323881
 
12.5%
1 323881
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 647762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3238810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 971643
30.0%
2 647762
20.0%
- 647762
20.0%
3 323881
 
10.0%
5 323881
 
10.0%
1 323881
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3238810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 971643
30.0%
2 647762
20.0%
- 647762
20.0%
3 323881
 
10.0%
5 323881
 
10.0%
1 323881
 
10.0%

PEILDATUM
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-05-01
323881 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3238810
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-01
2nd row2023-05-01
3rd row2023-05-01
4th row2023-05-01
5th row2023-05-01

Common Values

ValueCountFrequency (%)
2023-05-01 323881
100.0%

Length

2023-05-24T08:19:48.705271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-24T08:19:48.857475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-01 323881
100.0%

Most occurring characters

ValueCountFrequency (%)
0 971643
30.0%
2 647762
20.0%
- 647762
20.0%
3 323881
 
10.0%
5 323881
 
10.0%
1 323881
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2591048
80.0%
Dash Punctuation 647762
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 971643
37.5%
2 647762
25.0%
3 323881
 
12.5%
5 323881
 
12.5%
1 323881
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 647762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3238810
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 971643
30.0%
2 647762
20.0%
- 647762
20.0%
3 323881
 
10.0%
5 323881
 
10.0%
1 323881
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3238810
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 971643
30.0%
2 647762
20.0%
- 647762
20.0%
3 323881
 
10.0%
5 323881
 
10.0%
1 323881
 
10.0%

JAAR
Date

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Minimum2012-01-01 00:00:00
Maximum2023-01-01 00:00:00
2023-05-24T08:19:48.965444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:49.096604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean442.96919
Minimum301
Maximum8418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:49.259880image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum301
5-th percentile302
Q1305
median313
Q3322
95-th percentile361
Maximum8418
Range8117
Interquartile range (IQR)17

Descriptive statistics

Standard deviation1008.0077
Coefficient of variation (CV)2.2755707
Kurtosis58.499913
Mean442.96919
Median Absolute Deviation (MAD)8
Skewness7.7729381
Sum1.434693 × 108
Variance1016079.5
MonotonicityNot monotonic
2023-05-24T08:19:49.429545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
305 45708
14.1%
313 41933
12.9%
303 37351
11.5%
330 25543
 
7.9%
316 22002
 
6.8%
308 17494
 
5.4%
306 13567
 
4.2%
324 13306
 
4.1%
301 12905
 
4.0%
304 10483
 
3.2%
Other values (18) 83589
25.8%
ValueCountFrequency (%)
301 12905
 
4.0%
302 7088
 
2.2%
303 37351
11.5%
304 10483
 
3.2%
305 45708
14.1%
306 13567
 
4.2%
307 5674
 
1.8%
308 17494
 
5.4%
310 3569
 
1.1%
313 41933
12.9%
ValueCountFrequency (%)
8418 4488
 
1.4%
8416 596
 
0.2%
1900 213
 
0.1%
390 869
 
0.3%
389 3430
 
1.1%
362 4339
 
1.3%
361 2347
 
0.7%
335 3261
 
1.0%
330 25543
7.9%
329 837
 
0.3%
Distinct1899
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:49.922734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3522528
Min length2

Characters and Unicode

Total characters1085731
Distinct characters25
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row14
2nd row03
3rd row17
4th row01
5th row12
ValueCountFrequency (%)
101 1384
 
0.4%
402 1325
 
0.4%
403 1302
 
0.4%
301 1302
 
0.4%
201 1226
 
0.4%
203 1226
 
0.4%
401 1091
 
0.3%
404 1077
 
0.3%
802 1051
 
0.3%
409 1046
 
0.3%
Other values (1889) 311851
96.3%
2023-05-24T08:19:50.617703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 207822
19.1%
0 199075
18.3%
2 143985
13.3%
3 117583
10.8%
5 83783
7.7%
9 78106
 
7.2%
4 76968
 
7.1%
7 63960
 
5.9%
6 56719
 
5.2%
8 46752
 
4.3%
Other values (15) 10978
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1074753
99.0%
Uppercase Letter 10978
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
G 2055
18.7%
M 1851
16.9%
B 1320
12.0%
E 924
8.4%
Z 924
8.4%
D 732
 
6.7%
A 708
 
6.4%
F 685
 
6.2%
C 364
 
3.3%
K 352
 
3.2%
Other values (5) 1063
9.7%
Decimal Number
ValueCountFrequency (%)
1 207822
19.3%
0 199075
18.5%
2 143985
13.4%
3 117583
10.9%
5 83783
7.8%
9 78106
 
7.3%
4 76968
 
7.2%
7 63960
 
6.0%
6 56719
 
5.3%
8 46752
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1074753
99.0%
Latin 10978
 
1.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 2055
18.7%
M 1851
16.9%
B 1320
12.0%
E 924
8.4%
Z 924
8.4%
D 732
 
6.7%
A 708
 
6.4%
F 685
 
6.2%
C 364
 
3.3%
K 352
 
3.2%
Other values (5) 1063
9.7%
Common
ValueCountFrequency (%)
1 207822
19.3%
0 199075
18.5%
2 143985
13.4%
3 117583
10.9%
5 83783
7.8%
9 78106
 
7.3%
4 76968
 
7.2%
7 63960
 
6.0%
6 56719
 
5.3%
8 46752
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1085731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 207822
19.1%
0 199075
18.3%
2 143985
13.3%
3 117583
10.8%
5 83783
7.7%
9 78106
 
7.2%
4 76968
 
7.1%
7 63960
 
5.9%
6 56719
 
5.2%
8 46752
 
4.3%
Other values (15) 10978
 
1.0%

ZORGPRODUCT_CD
Real number (ℝ)

Distinct6065
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4256934 × 108
Minimum10501002
Maximum9.9841808 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:50.991570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum10501002
5-th percentile28999040
Q199899018
median1.49899 × 108
Q39.90004 × 108
95-th percentile9.9051605 × 108
Maximum9.9841808 × 108
Range9.8791708 × 108
Interquartile range (IQR)8.9010499 × 108

Descriptive statistics

Standard deviation4.293743 × 108
Coefficient of variation (CV)0.97018537
Kurtosis-1.7454639
Mean4.4256934 × 108
Median Absolute Deviation (MAD)1.2 × 108
Skewness0.4591178
Sum1.433398 × 1014
Variance1.8436229 × 1017
MonotonicityNot monotonic
2023-05-24T08:19:51.192041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
990004009 2325
 
0.7%
990004007 2294
 
0.7%
990003004 2269
 
0.7%
990004006 1882
 
0.6%
990356076 1692
 
0.5%
990356073 1575
 
0.5%
131999228 1499
 
0.5%
131999164 1483
 
0.5%
990003007 1471
 
0.5%
131999194 1361
 
0.4%
Other values (6055) 306030
94.5%
ValueCountFrequency (%)
10501002 9
< 0.1%
10501003 11
< 0.1%
10501004 12
< 0.1%
10501005 11
< 0.1%
10501007 3
 
< 0.1%
10501008 12
< 0.1%
10501010 12
< 0.1%
10501011 3
 
< 0.1%
11101002 10
< 0.1%
11101003 12
< 0.1%
ValueCountFrequency (%)
998418081 162
0.1%
998418080 147
< 0.1%
998418079 38
 
< 0.1%
998418077 8
 
< 0.1%
998418076 9
 
< 0.1%
998418075 7
 
< 0.1%
998418074 215
0.1%
998418073 216
0.1%
998418072 8
 
< 0.1%
998418071 8
 
< 0.1%

AANTAL_PAT_PER_ZPD
Real number (ℝ)

Distinct10272
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean527.24646
Minimum1
Maximum165128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:51.388463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median14
Q3105
95-th percentile1795
Maximum165128
Range165127
Interquartile range (IQR)102

Descriptive statistics

Standard deviation3237.2011
Coefficient of variation (CV)6.1398252
Kurtosis393.85328
Mean527.24646
Median Absolute Deviation (MAD)13
Skewness16.45387
Sum1.7076511 × 108
Variance10479471
MonotonicityNot monotonic
2023-05-24T08:19:51.576546image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 53452
 
16.5%
2 26088
 
8.1%
3 17054
 
5.3%
4 12435
 
3.8%
5 9745
 
3.0%
6 8283
 
2.6%
7 6852
 
2.1%
8 5768
 
1.8%
9 5274
 
1.6%
10 4704
 
1.5%
Other values (10262) 174226
53.8%
ValueCountFrequency (%)
1 53452
16.5%
2 26088
8.1%
3 17054
 
5.3%
4 12435
 
3.8%
5 9745
 
3.0%
6 8283
 
2.6%
7 6852
 
2.1%
8 5768
 
1.8%
9 5274
 
1.6%
10 4704
 
1.5%
ValueCountFrequency (%)
165128 1
< 0.1%
162302 1
< 0.1%
155873 1
< 0.1%
154260 1
< 0.1%
154256 1
< 0.1%
144717 1
< 0.1%
134885 1
< 0.1%
118397 1
< 0.1%
115938 1
< 0.1%
113442 1
< 0.1%

AANTAL_SUBTRAJECT_PER_ZPD
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct11021
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean624.25997
Minimum1
Maximum240002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:51.772338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median15
Q3115
95-th percentile2045
Maximum240002
Range240001
Interquartile range (IQR)112

Descriptive statistics

Standard deviation4170.5373
Coefficient of variation (CV)6.68077
Kurtosis700.23426
Mean624.25997
Median Absolute Deviation (MAD)14
Skewness21.005807
Sum2.0218594 × 108
Variance17393381
MonotonicityNot monotonic
2023-05-24T08:19:51.969006image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 51417
 
15.9%
2 25637
 
7.9%
3 16888
 
5.2%
4 12243
 
3.8%
5 9663
 
3.0%
6 8236
 
2.5%
7 6825
 
2.1%
8 5712
 
1.8%
9 5179
 
1.6%
10 4738
 
1.5%
Other values (11011) 177343
54.8%
ValueCountFrequency (%)
1 51417
15.9%
2 25637
7.9%
3 16888
 
5.2%
4 12243
 
3.8%
5 9663
 
3.0%
6 8236
 
2.5%
7 6825
 
2.1%
8 5712
 
1.8%
9 5179
 
1.6%
10 4738
 
1.5%
ValueCountFrequency (%)
240002 1
< 0.1%
232423 1
< 0.1%
231954 1
< 0.1%
230966 1
< 0.1%
227936 1
< 0.1%
227409 1
< 0.1%
226314 1
< 0.1%
223905 1
< 0.1%
218673 1
< 0.1%
215053 1
< 0.1%

AANTAL_PAT_PER_DIAG
Real number (ℝ)

Distinct9144
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7909.6674
Minimum1
Maximum230248
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:52.158165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile40
Q1416
median1780
Q36622
95-th percentile37704
Maximum230248
Range230247
Interquartile range (IQR)6206

Descriptive statistics

Standard deviation18196.621
Coefficient of variation (CV)2.3005546
Kurtosis33.20745
Mean7909.6674
Median Absolute Deviation (MAD)1622
Skewness4.9954146
Sum2.561791 × 109
Variance3.3111703 × 108
MonotonicityNot monotonic
2023-05-24T08:19:52.340441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 570
 
0.2%
2 536
 
0.2%
21 513
 
0.2%
17 493
 
0.2%
25 485
 
0.1%
8 475
 
0.1%
9 473
 
0.1%
3 472
 
0.1%
19 462
 
0.1%
5 461
 
0.1%
Other values (9134) 318941
98.5%
ValueCountFrequency (%)
1 570
0.2%
2 536
0.2%
3 472
0.1%
4 457
0.1%
5 461
0.1%
6 458
0.1%
7 416
0.1%
8 475
0.1%
9 473
0.1%
10 360
0.1%
ValueCountFrequency (%)
230248 23
< 0.1%
227944 23
< 0.1%
217927 24
< 0.1%
214511 17
< 0.1%
213519 25
< 0.1%
211593 17
< 0.1%
210419 19
< 0.1%
207311 19
< 0.1%
205347 17
< 0.1%
200603 16
< 0.1%
Distinct10213
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11446.022
Minimum1
Maximum369972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:52.527338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile52
Q1556
median2478
Q39449
95-th percentile53662
Maximum369972
Range369971
Interquartile range (IQR)8893

Descriptive statistics

Standard deviation27198.52
Coefficient of variation (CV)2.3762422
Kurtosis36.482962
Mean11446.022
Median Absolute Deviation (MAD)2275
Skewness5.2273769
Sum3.7071489 × 109
Variance7.3975947 × 108
MonotonicityNot monotonic
2023-05-24T08:19:52.712899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 478
 
0.1%
2 438
 
0.1%
3 408
 
0.1%
6 397
 
0.1%
25 383
 
0.1%
5 369
 
0.1%
17 366
 
0.1%
4 366
 
0.1%
20 360
 
0.1%
19 354
 
0.1%
Other values (10203) 319962
98.8%
ValueCountFrequency (%)
1 478
0.1%
2 438
0.1%
3 408
0.1%
4 366
0.1%
5 369
0.1%
6 397
0.1%
7 352
0.1%
8 306
0.1%
9 283
0.1%
10 332
0.1%
ValueCountFrequency (%)
369972 23
< 0.1%
364718 23
< 0.1%
348488 25
< 0.1%
343256 24
< 0.1%
341656 19
< 0.1%
323759 20
< 0.1%
315781 17
< 0.1%
310778 17
< 0.1%
298646 17
< 0.1%
294837 16
< 0.1%

AANTAL_PAT_PER_SPC
Real number (ℝ)

Distinct322
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean689067.34
Minimum3
Maximum1487638
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:52.911323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile42576
Q1297916
median765029
Q31032670
95-th percentile1340700
Maximum1487638
Range1487635
Interquartile range (IQR)734754

Descriptive statistics

Standard deviation411993.89
Coefficient of variation (CV)0.59790077
Kurtosis-1.0915012
Mean689067.34
Median Absolute Deviation (MAD)315911
Skewness-0.085927311
Sum2.2317582 × 1011
Variance1.6973897 × 1011
MonotonicityNot monotonic
2023-05-24T08:19:53.102518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
880937 5102
 
1.6%
874100 4354
 
1.3%
843977 4347
 
1.3%
894319 4333
 
1.3%
880479 4273
 
1.3%
897711 4212
 
1.3%
765029 4089
 
1.3%
802826 4026
 
1.2%
1080940 3890
 
1.2%
1100054 3866
 
1.2%
Other values (312) 281389
86.9%
ValueCountFrequency (%)
3 4
 
< 0.1%
6 2
 
< 0.1%
9 7
 
< 0.1%
16 3
 
< 0.1%
29 12
 
< 0.1%
33 9
 
< 0.1%
96 19
< 0.1%
116 28
< 0.1%
142 42
< 0.1%
151 1
 
< 0.1%
ValueCountFrequency (%)
1487638 2975
0.9%
1450399 3048
0.9%
1421728 3564
1.1%
1344384 3543
1.1%
1340700 3441
1.1%
1332379 3545
1.1%
1316489 3463
1.1%
1282947 3576
1.1%
1267063 3352
1.0%
1265243 1177
 
0.4%

AANTAL_SUBTRAJECT_PER_SPC
Real number (ℝ)

Distinct322
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1116593.1
Minimum3
Maximum2664930
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:53.304193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile46431
Q1515740
median1129339
Q31810699
95-th percentile2548762
Maximum2664930
Range2664927
Interquartile range (IQR)1294959

Descriptive statistics

Standard deviation738999.28
Coefficient of variation (CV)0.66183398
Kurtosis-0.81055633
Mean1116593.1
Median Absolute Deviation (MAD)632720
Skewness0.29566631
Sum3.616433 × 1011
Variance5.4611994 × 1011
MonotonicityNot monotonic
2023-05-24T08:19:53.502735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1211795 5102
 
1.6%
1281499 4354
 
1.3%
1216255 4347
 
1.3%
1315581 4333
 
1.3%
1300441 4273
 
1.3%
1341849 4212
 
1.3%
1155949 4089
 
1.3%
1203644 4026
 
1.2%
2548762 3890
 
1.2%
2664930 3866
 
1.2%
Other values (312) 281389
86.9%
ValueCountFrequency (%)
3 4
 
< 0.1%
6 2
 
< 0.1%
9 7
 
< 0.1%
16 3
 
< 0.1%
31 12
 
< 0.1%
35 9
 
< 0.1%
96 19
< 0.1%
129 28
< 0.1%
147 42
< 0.1%
154 1
 
< 0.1%
ValueCountFrequency (%)
2664930 3866
1.2%
2663352 3797
1.2%
2619520 3789
1.2%
2594184 3844
1.2%
2548762 3890
1.2%
2480464 3851
1.2%
2178864 3757
1.2%
2111895 3732
1.2%
2062429 3811
1.2%
2052309 1168
 
0.4%

GEMIDDELDE_VERKOOPPRIJS
Real number (ℝ)

Distinct3560
Distinct (%)1.3%
Missing49706
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean3605.7417
Minimum70
Maximum287220
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 MiB
2023-05-24T08:19:53.694474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum70
5-th percentile140
Q1480
median1270
Q34205
95-th percentile13710
Maximum287220
Range287150
Interquartile range (IQR)3725

Descriptive statistics

Standard deviation6555.1533
Coefficient of variation (CV)1.8179764
Kurtosis143.03878
Mean3605.7417
Median Absolute Deviation (MAD)1035
Skewness7.1204426
Sum9.8860424 × 108
Variance42970034
MonotonicityNot monotonic
2023-05-24T08:19:53.879505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160 2024
 
0.6%
105 1941
 
0.6%
110 1790
 
0.6%
180 1575
 
0.5%
185 1547
 
0.5%
300 1418
 
0.4%
145 1390
 
0.4%
175 1371
 
0.4%
120 1336
 
0.4%
165 1281
 
0.4%
Other values (3550) 258502
79.8%
(Missing) 49706
 
15.3%
ValueCountFrequency (%)
70 226
 
0.1%
75 75
 
< 0.1%
80 362
 
0.1%
85 919
0.3%
90 668
 
0.2%
95 716
 
0.2%
100 925
0.3%
105 1941
0.6%
110 1790
0.6%
115 1096
0.3%
ValueCountFrequency (%)
287220 8
< 0.1%
148910 3
 
< 0.1%
142835 4
< 0.1%
122155 4
< 0.1%
116765 3
 
< 0.1%
109725 7
< 0.1%
108570 7
< 0.1%
107655 4
< 0.1%
101270 8
< 0.1%
96945 5
< 0.1%

Interactions

2023-05-24T08:19:44.744129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:30.956380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:32.702622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:34.511290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:36.179396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:37.839980image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:39.470326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:41.199606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:43.067548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:44.940716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:31.161539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:32.900002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:34.707696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:36.377825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:38.031738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:39.672506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:41.405198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:43.267327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:45.126223image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:31.352514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:33.083463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:34.888617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:36.559382image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:38.210994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:39.860631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:41.588702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:43.451218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:45.310557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:31.546463image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:33.273935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:35.070528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:36.743007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:38.391138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:40.050816image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:41.780877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:43.637430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:45.491057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:31.732259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:33.454418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:35.251682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:36.919650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:38.566462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:40.237418image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:41.968320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:43.817394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:45.669718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:31.912250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:33.628414image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:35.423448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:37.091739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:38.734005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:40.415293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:42.295827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:43.993143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:45.861469image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:32.111871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:33.820776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:35.616671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:37.281644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:38.920507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:40.616232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:42.494029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:44.185147image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:46.057209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:32.313776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:34.014617image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:35.809636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:37.474245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:39.113102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:40.817908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:42.687005image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:44.378040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:46.243058image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:32.507280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:34.326861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:35.995169image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:37.655174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:39.291560image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:41.008622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:42.876229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-05-24T08:19:44.557103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-05-24T08:19:54.053136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BEHANDELEND_SPECIALISME_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
BEHANDELEND_SPECIALISME_CD1.0000.2180.0070.013-0.062-0.056-0.567-0.4770.050
ZORGPRODUCT_CD0.2181.000-0.141-0.150-0.178-0.211-0.387-0.4180.029
AANTAL_PAT_PER_ZPD0.007-0.1411.0000.9960.3250.3240.0750.083-0.305
AANTAL_SUBTRAJECT_PER_ZPD0.013-0.1500.9961.0000.3220.3240.0770.090-0.307
AANTAL_PAT_PER_DIAG-0.062-0.1780.3250.3221.0000.9880.3190.2980.023
AANTAL_SUBTRAJECT_PER_DIAG-0.056-0.2110.3240.3240.9881.0000.3310.3280.032
AANTAL_PAT_PER_SPC-0.567-0.3870.0750.0770.3190.3311.0000.958-0.015
AANTAL_SUBTRAJECT_PER_SPC-0.477-0.4180.0830.0900.2980.3280.9581.000-0.017
GEMIDDELDE_VERKOOPPRIJS0.0500.029-0.305-0.3070.0230.032-0.015-0.0171.000

Missing values

2023-05-24T08:19:46.629302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-24T08:19:47.329114image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
01.02023-05-102023-05-012018-01-013291499002901044979721981241681345.0
11.02023-05-102023-05-012018-01-01329039900290113753798478632198124168545.0
21.02023-05-102023-05-012018-01-0132917990029010737369570421981241681345.0
31.02023-05-102023-05-012018-01-0132901990029010535433134821981241681345.0
41.02023-05-102023-05-012018-01-0132912990029010141611412021981241681345.0
51.02023-05-102023-05-012018-01-0132920990029010224421981241681345.0
61.02023-05-102023-05-012018-01-013292099002901122442198124168545.0
71.02023-05-102023-05-012018-01-0132905990029002120124128513462198124168205.0
81.02023-05-102023-05-012018-01-0132916990029002161176107711962198124168205.0
91.02023-05-102023-05-012018-01-0132999990029011141141370838482198124168545.0
VERSIEDATUM_BESTANDPEILDATUMJAARBEHANDELEND_SPECIALISME_CDTYPERENDE_DIAGNOSE_CDZORGPRODUCT_CDAANTAL_PAT_PER_ZPDAANTAL_SUBTRAJECT_PER_ZPDAANTAL_PAT_PER_DIAGAANTAL_SUBTRAJECT_PER_DIAGAANTAL_PAT_PER_SPCAANTAL_SUBTRAJECT_PER_SPCGEMIDDELDE_VERKOOPPRIJS
3238711.02023-05-102023-05-012022-01-0130609114989901744196263469404698999970.0
3238721.02023-05-102023-05-012012-01-013032161992990704450225325148763819395093860.0
3238731.02023-05-102023-05-012022-01-01306025201100132236514694046989995490.0
3238741.02023-05-102023-05-012012-01-013034101920010621169079714876381939509NaN
3238751.02023-05-102023-05-012014-01-0131345213199914511455572103738820624294455.0
3238761.02023-05-102023-05-012015-01-01301809798990021128913466108442716533073410.0
3238771.02023-05-102023-05-012012-01-013031341319991311155446593148763819395095650.0
3238781.02023-05-102023-05-012012-01-013032581992990883322842527148763819395097575.0
3238791.02023-05-102023-05-012015-01-01301103798990141155332580031084427165330790.0
3238801.02023-05-102023-05-012014-01-01313431972802118111260413877103738820624297660.0